Lihuchen/pearl_benchmark

PEARL-Benchmark: A benchmark for evaluating phrase representations

Learning High-Quality and General-Purpose Phrase Representations.
Lihu Chen, Gaël Varoquaux, Fabian M. Suchanek. Accepted by EACL Findings 2024

Our PEARL Benchmark contains 9 phrase-level datasets of five types of tasks, which cover both the field of data science and natural language processing.

Description

• Paraphrase Classification: PPDB and PPDBfiltered (Wang et al., 2021)
• Phrase Similarity: Turney (Turney, 2012) and BIRD (Asaadi et al., 2019)
• Entity Retrieval: We constructed two datasets based on Yago (Pellissier Tanon et al., 2020) and UMLS (Bodenreider, 2004)
• Entity Clustering: CoNLL 03 (Tjong Kim Sang, 2002) and BC5CDR (Li et al., 2016)
• Fuzzy Join: AutoFJ benchmark (Li et al., 2021), which contains 50 diverse fuzzy-join datasets

  -	PPDB	PPDB filtered	Turney	BIRD	YAGO	UMLS	CoNLL	BC5CDR	AutoFJ
  Task	Paraphrase Classification	Paraphrase Classification	Phrase Similarity	Phrase Similarity	Entity Retrieval	Entity Retrieval	Entity Clustering	Entity Clustering	Fuzzy Join
  Samples	23.4k	15.5k	2.2k	3.4k	10k	10k	5.0k	9.7k	50 subsets
  Averaged Length	2.5	2.0	1.2	1.7	3.3	4.1	1.5	1.4	3.8
  Metric	Acc	Acc	Acc	Pearson	Top-1 Acc	Top-1 Acc	NMI	NMI	Acc

Usage

from datasets import load_dataset
turney_dataset = load_dataset("Lihuchen/pearl_benchmark", "turney", split="test")

Evaluation

We offer a python script to evaluate your model: eval.py

python eval.py -batch_size 32

Citation

@article{chen2024learning,
  title={Learning High-Quality and General-Purpose Phrase Representations},
  author={Chen, Lihu and Varoquaux, Ga{\"e}l and Suchanek, Fabian M},
  journal={arXiv preprint arXiv:2401.10407},
  year={2024}
}